





'''simple docstring'''


from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


a__			:					Dict       						=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class     lowerCAmelCase__ (				UpperCAmelCase_      , UpperCAmelCase_    ):

       '''simple docstring'''

       @register_to_config
       def __init__( self					:				int       ,     a__					:				bool       ,     a__					:				Optional[int] = None       ,     a__					:				Optional[int] = None						):
              super().__init__()

              UpperCAmelCase  =						learnable

              if self.learnable:
                     assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
                     assert length is not None, "learnable=True requires `length` to be set"

                     UpperCAmelCase  =						torch.zeros(a__       ,     a__						)
              else:
                     UpperCAmelCase  =						None

              UpperCAmelCase  =						torch.nn.Parameter(a__						)






class     lowerCAmelCase__ (				UpperCAmelCase_    ):

       '''simple docstring'''
       _lowerCamelCase								=42
       _lowerCamelCase								=42
       _lowerCamelCase								=42
       _lowerCamelCase								=42
       _lowerCamelCase								=42
       _lowerCamelCase								=42

       def __init__( self					:				List[str]       ,     a__					:				VQModel       ,     a__					:				CLIPTextModel       ,     a__					:				CLIPTokenizer       ,     a__					:				TransformeraDModel       ,     a__					:				VQDiffusionScheduler       ,     a__					:				LearnedClassifierFreeSamplingEmbeddings       ,     ):
              super().__init__()

              self.register_modules(
                  vqvae=a__       ,     transformer=a__       ,     text_encoder=a__       ,     tokenizer=a__       ,     scheduler=a__       ,     learned_classifier_free_sampling_embeddings=a__       ,     )

       def 			__snake_case      ( self					:				Any       ,     a__					:				Dict       ,     a__					:				Optional[int]       ,     a__					:				Optional[Any]						):
              UpperCAmelCase  =						len(a__						) if isinstance(a__       ,     a__						) else 1

              # get prompt text embeddings
              UpperCAmelCase  =						self.tokenizer(
                  a__       ,     padding='''max_length'''       ,     max_length=self.tokenizer.model_max_length       ,     return_tensors='''pt'''       ,     )
              UpperCAmelCase  =						text_inputs.input_ids

              if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                     UpperCAmelCase  =						self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]						)
                     logger.warning(
                         '''The following part of your input was truncated because CLIP can only handle sequences up to'''
                         f" {self.tokenizer.model_max_length} tokens: {removed_text}"						)
                     UpperCAmelCase  =						text_input_ids[:, : self.tokenizer.model_max_length]
              UpperCAmelCase  =						self.text_encoder(text_input_ids.to(self.device						)						)[0]

              # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
              # While CLIP does normalize the pooled output of the text transformer when combining
              # the image and text embeddings, CLIP does not directly normalize the last hidden state.
              #
              # CLIP normalizing the pooled output.
              # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
              UpperCAmelCase  =						prompt_embeds / prompt_embeds.norm(dim=-1       ,     keepdim=a__						)

              # duplicate text embeddings for each generation per prompt
              UpperCAmelCase  =						prompt_embeds.repeat_interleave(a__       ,     dim=0						)

              if do_classifier_free_guidance:
                     if self.learned_classifier_free_sampling_embeddings.learnable:
                            UpperCAmelCase  =						self.learned_classifier_free_sampling_embeddings.embeddings
                            UpperCAmelCase  =						negative_prompt_embeds.unsqueeze(0						).repeat(a__       ,     1       ,     1						)
                     else:
                            UpperCAmelCase  =						[''''''] * batch_size

                            UpperCAmelCase  =						text_input_ids.shape[-1]
                            UpperCAmelCase  =						self.tokenizer(
                                a__       ,     padding='''max_length'''       ,     max_length=a__       ,     truncation=a__       ,     return_tensors='''pt'''       ,     )
                            UpperCAmelCase  =						self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]
                            # See comment for normalizing text embeddings
                            UpperCAmelCase  =						negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1       ,     keepdim=a__						)

                     # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                     UpperCAmelCase  =						negative_prompt_embeds.shape[1]
                     UpperCAmelCase  =						negative_prompt_embeds.repeat(1       ,     a__       ,     1						)
                     UpperCAmelCase  =						negative_prompt_embeds.view(batch_size * num_images_per_prompt       ,     a__       ,     -1						)

                     # For classifier free guidance, we need to do two forward passes.
                     # Here we concatenate the unconditional and text embeddings into a single batch
                     # to avoid doing two forward passes
                     UpperCAmelCase  =						torch.cat([negative_prompt_embeds, prompt_embeds]						)

              return prompt_embeds

       @torch.no_grad()
       def __call__( self					:				Optional[Any]       ,     a__					:				Union[str, List[str]]       ,     a__					:				int = 100       ,     a__					:				float = 5.0       ,     a__					:				float = 1.0       ,     a__					:				int = 1       ,     a__					:				Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,     a__					:				Optional[torch.FloatTensor] = None       ,     a__					:				Optional[str] = "pil"       ,     a__					:				bool = True       ,     a__					:				Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,     a__					:				int = 1       ,     ):
              if isinstance(a__       ,     a__						):
                     UpperCAmelCase  =						1
              elif isinstance(a__       ,     a__						):
                     UpperCAmelCase  =						len(a__						)
              else:
                     raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(a__						)}"						)

              UpperCAmelCase  =						batch_size * num_images_per_prompt

              UpperCAmelCase  =						guidance_scale > 1.0

              UpperCAmelCase  =						self._encode_prompt(a__       ,     a__       ,     a__						)

              if (callback_steps is None) or (
                  callback_steps is not None and (not isinstance(a__       ,     a__						) or callback_steps <= 0)
              ):
                     raise ValueError(
                         f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                         f" {type(a__						)}."						)

              # get the initial completely masked latents unless the user supplied it

              UpperCAmelCase  =						(batch_size, self.transformer.num_latent_pixels)
              if latents is None:
                     UpperCAmelCase  =						self.transformer.num_vector_embeds - 1
                     UpperCAmelCase  =						torch.full(a__       ,     a__						).to(self.device						)
              else:
                     if latents.shape != latents_shape:
                            raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"						)
                     if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
                            raise ValueError(
                                '''Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'''
                                f" {self.transformer.num_vector_embeds - 1} (inclusive)."						)
                     UpperCAmelCase  =						latents.to(self.device						)

              # set timesteps
              self.scheduler.set_timesteps(a__       ,     device=self.device						)

              UpperCAmelCase  =						self.scheduler.timesteps.to(self.device						)

              UpperCAmelCase  =						latents

              for i, t in enumerate(self.progress_bar(a__						)						):
                     # expand the sample if we are doing classifier free guidance
                     UpperCAmelCase  =						torch.cat([sample] * 2						) if do_classifier_free_guidance else sample

                     # predict the un-noised image
                     # model_output == `log_p_x_0`
                     UpperCAmelCase  =						self.transformer(a__       ,     encoder_hidden_states=a__       ,     timestep=a__						).sample

                     if do_classifier_free_guidance:
                            UpperCAmelCase,					UpperCAmelCase  =						model_output.chunk(2						)
                            UpperCAmelCase  =						model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
                            model_output -= torch.logsumexp(a__       ,     dim=1       ,     keepdim=a__						)

                     UpperCAmelCase  =						self.truncate(a__       ,     a__						)

                     # remove `log(0)`'s (`-inf`s)
                     UpperCAmelCase  =						model_output.clamp(-70						)

                     # compute the previous noisy sample x_t -> x_t-1
                     UpperCAmelCase  =						self.scheduler.step(a__       ,     timestep=a__       ,     sample=a__       ,     generator=a__						).prev_sample

                     # call the callback, if provided
                     if callback is not None and i % callback_steps == 0:
                            callback(a__       ,     a__       ,     a__						)

              UpperCAmelCase  =						self.vqvae.config.vq_embed_dim
              UpperCAmelCase  =						(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
              UpperCAmelCase  =						self.vqvae.quantize.get_codebook_entry(a__       ,     shape=a__						)
              UpperCAmelCase  =						self.vqvae.decode(a__       ,     force_not_quantize=a__						).sample

              UpperCAmelCase  =						(image / 2 + 0.5).clamp(0       ,     1						)
              UpperCAmelCase  =						image.cpu().permute(0       ,     2       ,     3       ,     1						).numpy()

              if output_type == "pil":
                     UpperCAmelCase  =						self.numpy_to_pil(a__						)

              if not return_dict:
                     return (image,)

              return ImagePipelineOutput(images=a__						)



       def 			__snake_case      ( self					:				List[str]       ,     a__					:				torch.FloatTensor       ,     a__					:				float						):
              UpperCAmelCase,					UpperCAmelCase  =						torch.sort(a__       ,     1       ,     descending=a__						)
              UpperCAmelCase  =						torch.exp(a__						)
              UpperCAmelCase  =						sorted_p_x_0.cumsum(dim=1						) < truncation_rate

              # Ensure that at least the largest probability is not zeroed out
              UpperCAmelCase  =						torch.full_like(keep_mask[:, 0:1, :]       ,     a__						)
              UpperCAmelCase  =						torch.cat((all_true, keep_mask)       ,     dim=1						)
              UpperCAmelCase  =						keep_mask[:, :-1, :]

              UpperCAmelCase  =						keep_mask.gather(1       ,     indices.argsort(1						)						)

              UpperCAmelCase  =						log_p_x_0.clone()

              UpperCAmelCase  =						-torch.inf  # -inf = log(0)

              return rv







def  lowerCAmelCase_	(lowerCAmelCase__:		list  ):





			"""simple docstring"""


			if len(lowerCAmelCase__  ) <= 1:
						return [tuple(lowerCAmelCase__  )]

			UpperCAmelCase_:		List[Any]							      =     []

			def generate(lowerCAmelCase__:		int		,						lowerCAmelCase__:		list  ):
						if k == 1:
									res.append(tuple(arr[:]  )  )
									return

						generate(k - 1		,						lowerCAmelCase__  )

						for i in range(k - 1  ):
									if k % 2 == 0:  # k is even
												UpperCAmelCase_      ,							UpperCAmelCase_:		Union[str, Any]							      =     arr[k - 1], arr[i]
									else:  # k is odd
												UpperCAmelCase_      ,							UpperCAmelCase_:		Optional[int]							      =     arr[k - 1], arr[0]
									generate(k - 1		,						lowerCAmelCase__  )

			generate(len(lowerCAmelCase__  )		,						lowerCAmelCase__  )
			return res


if __name__ == "__main__":
	a      :  Optional[Any]			     =			input('Enter numbers separated by a comma:\n').strip()
	a      :  str			     =			[int(item) for item in user_input.split(',')]
	print(heaps(arr))






